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Improving generative adversarial network with multiple generators by evolutionary algorithms

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Abstract

Generative Adversarial Network (GAN) is a novel class of deep generative models that has recently gained significant attention. However, the original GAN with one generator can easily get trapped into the mode collapsing problem, which could cause the generator only to produce similar images. This paper proposed a combination of GAN and an evolutionary algorithm to overcome the mode collapsing problem. In our approach, multiple generator networks are trained with the evolutionary strategy (ES), an evolution algorithm. The discriminator network distinguishes if the image comes from the real dataset or not. An additional classifier network is implemented to distinguish different generators. The mutations in the evolutionary strategy and the additional classifier network keep the diversity among generators. We term our approach the Evolution-GAN. In this paper, we conduct experiments on 2D synthetic data to verify that the Evolution-GAN overcomes the mode collapsing problem. Furthermore, experiments on MNIST datasets are implemented to compare the performance of Evolution-GAN, the original GAN, and Deep Convolutional GAN(DCGAN) and Evolutionary GAN.

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Author notes

  1. Yupeng Liang is the presenter of this paper.

Authors and Affiliations

  1. Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima, Japan

    Yupeng Liang, Ziyao Han, Xiaotong Nie & Kazuhiro Ohkura

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  1. Yupeng Liang
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  2. Ziyao Han
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  3. Xiaotong Nie
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  4. Kazuhiro Ohkura
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Correspondence to Yupeng Liang.

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This work was presented in part at the joint symposium of the 27th International Symposium on Artificial Life and Robotics, the 7th International Symposium on BioComplexity, and the 5th International Symposium on Swarm Behavior and Bio-Inspired Robotics (Online, January 25-27, 2022).

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Liang, Y., Han, Z., Nie, X. et al. Improving generative adversarial network with multiple generators by evolutionary algorithms. Artif Life Robotics 27, 761–769 (2022). https://doi.org/10.1007/s10015-022-00801-7

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  • DOI: https://doi.org/10.1007/s10015-022-00801-7

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